A Study on the Identification of Glass Artefacts Based on Multiple Logistic Regression

Yujie Han

doi:10.54097/hset.v41i.6837

Authors

Yujie Han

DOI:

https://doi.org/10.54097/hset.v41i.6837

Keywords:

Multiple logistic regression, BP neural network, glass classification.

Abstract

In this paper, traditional glass artefacts are classified into four categories, and the common factors obtained from factor analysis are used to solve a multivariate logistic regression model to avoid the 'pseudo-regression' caused by multiple covariance, and then predict the unknown types of glass artefacts. The BP neural network structure was also constructed based on the data, and the optimal solution was obtained after 1000 iterations using Bayesian regularization to minimize the sum of squares of the errors, provided that 10 neurons were used. The glass samples were identified to belong to the categories of unweathered high potassium, weathered lead barium, unweathered lead barium, unweathered lead barium, weathered lead barium, weathered high potassium, weathered high potassium and unweathered lead barium.

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